U.S. patent number 5,204,878 [Application Number 07/620,710] was granted by the patent office on 1993-04-20 for method of effecting channel estimation for a fading channel when transmitting symbol sequences.
This patent grant is currently assigned to Telefonaktiebolaget L M Ericsson. Invention is credited to Lars G. Larsson.
United States Patent |
5,204,878 |
Larsson |
April 20, 1993 |
Method of effecting channel estimation for a fading channel when
transmitting symbol sequences
Abstract
Symbols in a symbol sequence transmitted via a fading channel
are estimated in an equalizer having a channel estimating filter.
An error signal influences an adaptation circuit which controls
adjustable coefficients so as to form a channel estimate, which is
adapted during the whole of the symbol sequence. The adaptation
circuit performs an adaptation algorithm, which is influenced by
mean energy values obtained from the channel estimate in a
mean-value-forming circuit. Subsequent to fading the adaptation
algorithm is able to control the coefficients so that one of the
coefficients will erroneously dominate the channel estimate, with
erroneous estimation of symbols as a result. This is counteracted
by selecting the largest of the mean energy values, this largest
value influencing the adaptation algorithm. When the signal
strength of the transmitted symbols falls beneath a threshold
value, the coefficient which dominated the channel estimate, for
instance the first coefficient is sustained to also dominate after
fading and the remaining coefficients are set to zero.
Inventors: |
Larsson; Lars G.
(Svartviksslingan, SE) |
Assignee: |
Telefonaktiebolaget L M
Ericsson (Stockholm, SE)
|
Family
ID: |
20377842 |
Appl.
No.: |
07/620,710 |
Filed: |
December 3, 1990 |
Foreign Application Priority Data
|
|
|
|
|
Dec 22, 1989 [SE] |
|
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8904327-7 |
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Current U.S.
Class: |
375/232;
375/341 |
Current CPC
Class: |
H04B
17/318 (20150115); H04B 7/005 (20130101) |
Current International
Class: |
H04B
7/005 (20060101); H04B 17/00 (20060101); H04L
027/01 () |
Field of
Search: |
;364/724.2 ;371/43,44,45
;375/11,13,14,94,106 |
References Cited
[Referenced By]
U.S. Patent Documents
Other References
Dominique N. Godard, "Self-Recovering Equalization and Carrier
Tracking in Two Dimensional Data Communication Systems", IEEE
Transactions on Communications, vol. COM-28, No. 11, Nov. 1980.
.
William C. Y. Lee, "Received-Signal Envelope Characteristics" and
Received-Signal Phase Characteristics, Mobile Communications
Engineering, Chapters 6 and 7, pp. 169-233. .
"Adaptive Maximum-Likelihood Sequence Estimation for Digital
Signaling in the Presence of Intersymbol Interference" in IEEE
Transactions of Information Theory, Jan. 1973, pp. 120-124 by
Magee, Jr. and Proakis..
|
Primary Examiner: Safourek; Benedict V.
Attorney, Agent or Firm: Burns, Doane, Swecker &
Mathis
Claims
What is claimed is:
1. A method for effecting channel estimation for a fading channel
when transmitting a signal comprising symbol sequences, wherein
each of the symbol sequences has at least one synchronizing
sequence and one data sequence, said method comprising the steps
of:
sampling said signal at a series of sampling time points to obtain
a series of signal samples;
generating a channel estimate having plural coefficients, based on
knowledge of said synchronizing sequence, for said fading channel
at each sampling time point;
estimating values of symbols in a symbol sequence of said signal
based on said signal samples and said channel estimate;
adapting said channel estimate according to an adaptation
algorithm, based on knowledge of said channel estimate as applied
to signal samples corresponding to said data sequence;
calculating mean energy values of said channel estimate by forming
a time mean value over multiple sampling time points of energy
represented by each of the coefficients of the channel
estimate;
selecting at least the largest of the mean energy values; and
influencing the adaptation algorithm in dependence on the selected
mean energy values, such that adaptation of the channel estimate is
controlled with the aid of the selected mean energy values enabling
a correct estimation to be made of symbols in a remaining part of
the symbol sequence subsequent to fading during the symbol
sequence.
2. A method according to claim 1 wherein said mean energy values
are calculated over one or more whole symbol sequences.
3. A method according to claim 1 wherein said mean energy values
are calculated over solely the synchronizing sequence of one or
more of the symbol sequences.
4. A method according to claim 1, in which the adaptation algorithm
is a least mean square algorithm which, at the sampling time
points, calculates a value for the channel estimate with the aid of
an error signal at the latest sampling time point, and comprising
the further step of determining weighing factors based on the
selected mean energy values, wherein the adaptation algorithm is
influenced by weighing said error signal into the channel estimate
with the aid of said weighing factors.
5. A method according to claim 4, wherein the weighing factors are
proportional to the square root of a quotient between the mean
energy value that corresponds to each weighing factor and the
largest mean energy value.
6. A method according to claim 1, in which the algorithm is a least
mean square algorithm which, at the sampling time points,
calculates a value for the channel estimate based on the channel
estimate for the preceding sampling time point, further comprising
the steps of:
detecting signal strength of the transmitted signal;
determining a threshold value for the signal strength;
determining the occurrence of fading by comparing the signal
strength with the threshold value; and
in the event of fading, causing the adaptation algorithm to form
the channel estimate based solely on those coefficients in the
channel estimate for the preceding sampling time point which
correspond to the selected mean energy values.
7. A method according to claim 6, wherein the threshold value for
the signal strength is a predetermined value.
8. A method according to claim 6, wherein the threshold value of
the signal strength is proportional to the square root of the
quotient between the largest and the next largest of the man energy
values.
Description
TECHNICAL FIELD
The present invention relates to a method for effecting channel
estimation for a fading channel when transmitting symbol sequences,
wherein each of the symbol sequences has at least one synchronizing
sequence and one data sequence, said method comprising the steps
of:
effecting a channel estimation partly with the aid of the
synchronizing sequence and partly by adaptation during the data
sequence with the aid of a desired adaptation algorithm, wherein at
least one channel estimate is obtained at each sampling time point,
and
effecting channel equalization with the aid of the channel estimate
and estimation of symbols in the symbol sequences.
BACKGROUND ART
One problem which often occurs in radio transmissions over a
channel is that a transmitted signal is subjected to multipath
propagation and noise. In the case of mobile telephony for
instance, the transmission properties of the channel will shift due
to a change in the mutual positions of the transmitter and
receiver. These problems have been solved in time-shared, digital
radio transmission systems, in that the time-slot transmitted
signal sequences include a synchronizing sequence and a data
sequence. The synchronizing sequence is known to the receiver and
the receiver is able to evaluate the transmission properties of the
channel, i.e. effect a channel estimate, on the basis of this
sequence. With the aid of this channel estimate, the receiver is
able to evaluate the symbols of the data sequence which contains
the information to be transmitted.
However, in certain instances it has been found insufficient to
effect a channel estimate only once with each time slot. In the
case of time slots of long duration, i.e. in the order of several
milliseconds, the transmitter and receiver have sufficient time to
change their mutual positions considerably during the course of a
time slot. Consequently, the transmission properties of the channel
can change radically during the duration of the time slot, so that
the receiver estimation of the transmitted symbols becomes
deficient and the transmitted information contains interferences. A
radio receiver in which these interferences are partially avoided
is found described in an article in IEEE Transactions On
Information Theory, January 1973, pages 120-124, F. R. Magee Jr and
J. G. Proakis: "Adaptive Maximum-Likelihood Sequence Estimation for
Digital Signaling in the Presence of Intersymbol Interference". The
article describes an equalizer which includes a viterbianalyzer
equipped with an adaptation filter as a channel estimating
circuit.
The equalizer described in this article partially overcomes those
problems which occur with long time slots, although it has the
disadvantage of lacking the ability to perform a correct adaptation
after fading, during which the signal strength falls beneath the
noise level. After fading has taken place the equalizer has
difficulty in re-adapting to the data sequence, which is a sequence
unknown to the receiver.
Fading occurs as a result of signal interference between signals
reflected along mutually separate paths, such that fading often
recurs for a mobile receiver which moves in the interference
pattern of the signals. This can result in a large proportion of
transmitted signal sequences being subjected to fading, so that a
large part of the transmitted information will be lost. A solution
to the problem of adapting to an unknown signal sequence is given
in an article in IEEE Transactions on Communications, Vol Com-28,
No. 11, November 1980, D. N. Godard: "Self-Recovering Equalization
and Carrier Tracking in Two-Dimensional Data Communication
Systems". Equalizer adaptation in the event of intersymbol
interference is achieved by the introduction of an algorithm with a
new type of cost functions and by minimizing these functions. The
algorithm, however, converges relatively slowly and cannot be
utilized when desiring to adapt an equalizer during one of the
aforesaid time slots with a duration in the order of
milliseconds.
DISCLOSURE OF THE INVENTION
The aforesaid problem of adapting an equalizer rapidly with the aid
of an unknown signal is solved in accordance with the invention by
forming successively the mean energy values of the channel
estimate. This formation of the mean values is effected over a
period of time of such long duration as to render the influence of
fading on the mean values negligible. An adaptation algorithm is
influenced by the mean energy values formulated in a manner to
obtain renewed, correct channel estimation after fading.
The invention is characterized by the features set forth in the
following claims.
BRIEF DESCRIPTION OF THE DRAWINGS
An exemplifying embodiment of the invention will now be described
in more detail with reference to the accompanying drawings, in
which
FIG. 1 illustrates schematically a radio transmission system
comprising a transmitter, a receiver and an intermediate disturbed
channel;
FIG. 2 illustrates time slots for a time-shared transmission system
and a time slot signal sequence;
FIG. 3 is a diagram which shows the separate values of a
transmitted symbol;
FIG. 4 illustrates a mobile receiver which moves in an interference
pattern between two buildings;
FIG. 5 is a diagram which shows the variation in signal strength
during a signal sequence;
FIG. 6 is a block schematic of a viterbi equalizer provided with an
inventive channel estimation filter;
FIG. 7 is a circuit diagram of the inventive channel estimation
filter; and
FIG. 8 is a block schematic which illustrates an alternative
viterbi equalizer provided with an inventive channel estimation
filter.
BEST MODE OF CARRYING OUT THE INVENTION
FIG. 1 illustrates schematically a known radio transmission system
for time-shared radio communication. A transmitter has a unit (1)
which generates digital symbols S(n). These symbols are
digital/analogue converted and transmitted as a signal Y from a
unit 2 to a receiving unit 3 of a receiver. The received signal is
filtered and sampled in unit 4 to form a received digital signal
y(n), which is delivered to a channel equalizer 5. This equalizer
delivers at given time delays, estimated symbols S(n-L) which
constitute an estimation of the transmitted signals S(n). The sign
(n) denotes a sampling time point with number n and the reference
sign (n-L) denotes that the estimated symbols are delayed by a
number of L sampling intervals. The double signal paths in FIG. 1
indicate that the channel between the units 2 and 3 subjects the
transmitted signal Y to time dispersion. A disturbance signal on
the same channel as that used between the units 2 and 3 is
indicated by a signal A. As will be explained herebelow,
transmission is also disturbed by signal fading. As
beforementioned, the radio transmission system is time-shared with
mutually separate time slots 1-N, according to FIG. 2, in which the
capital letter T indicates time. It is possible to transmit in each
time slot f a signal sequence SS which includes a synchronizing
sequence SO and a data sequence DO that contains the information to
be transmitted. The signal sequence SS includes binary signals,
although the aforesaid symbols S(n) are modulated in accordance,
for instance, with QPSK-modulation, as illustrated in FIG. 3. In a
complex speech plan with the axes referenced I and Q, the four
possible values of the symbols S(n) are marked one in each quadrant
with the binary numbers 00, 01, 10 or 11. The time taken to
transmit one such modulated symbol is referred to as a symbol time
TS.
The aforesaid signal fading, so-called Rayleigh-fading, occurs in
the following way. FIG. 4 illustrates two buildings 20 and 21 which
reflect the transmitted signal Y. It is assumed in this case that
essentially one of the signal paths in FIG. 1 reaches the region
between the buildings 20 and 21, so-called single beam propagation
of the signal Y. The reflected signals interfer with one another
between the buildings. When the difference in propagation time of
interfering signals is less than approximately TS/4, a regular
interference pattern can occur with alternating maxima and nodes in
signal strength. A mobile receiver 22 which moves through the
interference pattern will repeatedly pass the nodes, where the
signal strength is very low. A more exhaustive description of
fading is found in William C. Y. Lee: Mobile Communications
Engineering, Chapter 6 and 7, McGRaw-Hill, Inc. 1982.
FIG. 5 shows a curve 23 which illustrates how the signal strength,
the absolute value of Y and referenced F, can vary in respect of
the mobile 22 during the time duration of the signal sequence SS.
The noise level is shown with a broken line 24 and the Figure
illustrates how the signals strength F falls beneath the noise
level during a time interval TF.
As mentioned in the aforegoing, the channel equalizer 5 of the
mobile 22 is preferably adaptive in the case of long signal
sequences SS, which have a time duration of several milliseconds.
The adaptation filter of the equalizer can then be adapted to
quickly shifting transmission properties of the transmission
channel. In the case of known filters, however, this adaptation
possibility has the disadvantage that said filters will also adapt
to the low signal strength when the fading illustrated in FIG. 5
occurs. When the signal strength F increases subsequent to fading,
it is possible that erroneous adaptation will occur such that the
estimated signal S (n-L) will have a large bit-error content and
that the information in the signal sequence SS will be lost after
fading. The object of the present invention is to enable adaptive
channel estimation to be effected in the equalizer 5 without this
disadvantage.
As will be seen from the schematic illustration of FIG. 6, the
equalizer 5 comprises a viterbi analyzer VIT, an adaptive channel
estimating filter CEST, and a delay circuit DEL. The viterbi
analyzer VIT receives the signal y(n) and produces the symbols
S(n-L), which have been estimated in a known manner with the delay
of L sampling steps. The channel estimating filter CEST receives
the estimated symbols S (n-L) and also the signals y(n-L), which
are the received signals y(n) delayed through L sampling steps in
the delay circuit DEL. The channel estimating filter CEST receives
the signal y(n-L) and the estimated symbols S (n-L), and delivers
to the viterbi analyzer VIT an estimated impulse response, a
channel estimate, C(n) for the channel. It should be noted that in
addition to including the actual radio channel itself, the channel
estimate also includes transmitter and receiver filters.
Alternatively, a preliminary decision from the viterbi analyzer VIT
can be utilized instead of the estimated symbols S (n-L). This will
result in a delay which is shorter than the L sampling steps or
intervals. Estimation of the impulse response C(n) will be
described in more detail herebelow with reference to FIG. 7. The
viterbi equalizer 5 utilizes the synchronizing sequence SO to
create a start value for C(n), which is then updated for each new
sampling time point n.
The channel estimating filter CEST, shown in more detail in FIG. 7,
includes the delay element 6, adjustable coefficients 7, summators
8, a difference former 9 and an adaptation circuit 10 which
performs an adaptation algorithm. The adaptation circuit 10 is
controlled from a mean-value formation circuit 15 which forms a
time mean value U(n) of the energy in the channel estimate C(n).
The number K of coefficients 7 will depend on the magnitude of time
dispersion the channel can have expressed in a number of sampling
intervals, and in the illustrated example K=3. The estimated
symbols S(n-L) are delayed stepwise by one sampling time point in
the delay elements 6 to form symbols S(n-L-1) up to S(n-L-K+1)
which are multiplied by coefficients c.sub.l (n) ...c.sub.k (n).
Subsequent to stepwise addition in the summators 8 to form a
filtered estimated signal y(n-L) there is formed an error signal
e(n) which is the difference between the signal y(n-L) and the
delayed signal y(n-L) received. The adaptation circuit 10 receives
the error signal e(n) and controls the coefficients 7 so as to
minimize the error signal. The coefficients c.sub.l (n), ...c.sub.k
(n) constitute the aforesaid estimated impulse response C(n). This
can be described as a channel vector C(n)={c.sub.l (n) .... c.sub.k
(n)}.sup.T, and correspondingly a signal vector can be defined by
the relationship S(n)={s(n-L), ....s(n-L-K+1)}.sup.T. With the aid
of these vectors, the error signal index T denotes a transposition.
By formation of the mean value of the channel estimate energy C(n)
is meant a formation of a time mean value u.sub.i (n) of the energy
contained by each of the coefficients c.sub.l (n)....c.sub.k (n)
The mean value of the channel estimate energy U(n) can be expressed
as U(n)={u.sub.l (n)....u.sub.h (n),....u.sub.k (n)}.sup.T, which
is calculated in accordance with the relationship
U(n)=U(n-1)+G{.vertline.C(n).vertline..sup.2 -U(n-1)}. In this
connection, G is a constant which expresses the duration of the
time interval over which formation of the mean values takes place.
The duration of this time interval is selected so that during the
time interval TF fading will have a negligible influence on the
mean value, and the time interval can extend over several signal
sequences SS. Primarily, only one synchronizing sequence SO in each
signal sequence SS is utilized in the formation of a mean value. It
is also possible, however, in accordance with the invention to
utilize the whole of the signal sequence SS, so that all of the
subsequently adapted channel estimates C(n) will be included in the
calculation of the mean value U(n). The adaptive algorithm
performed in the circuit 10 is controlled with the aid of the time
mean value U(n) of the impulse response energy. For instance, when
the adaptation algorithm is an LMS-algorithm, Least Mean Square,
the channel estimate is calculated iteratively in accordance with
the relationship C(n)=Q.times.C(n-1)+M.times.S*(n)xe(n). In this
case the index * is a complex conjugation. Q is a diagonal matrix
with diagonal elements q.sub.l,...q.sub.h,...q.sub.k and M is a
diagonal matrix with diagonal elements
M=.mu..sub.1,....mu..sub.h,....mu. .sub.k. According to one
alternative embodiment, this adaptation algorithm can be controlled
in the following manner. The circuit 15 which forms said mean value
detects when fading prevails through the signal y(n-L), i.e. when
the signal strength F falls beneath a threshold value FO, see FIG.
5. In the event of fading, the matrix M is constantly unaffected.
One of the coefficients, for instance q.sub.h, in the matrix Q is
selected and is set to a desired value, for instance q.sub.h =1.
The remaining coefficients are set to zero. The choice of
coefficient is effected with the aid of the mean value of the
channel estimate energy U(n), so that the selected coefficient
q.sub.h will correspond to the largest coefficient u.sub.h (n) in
the mean value U(n). As a result, that coefficient of the
coefficients in the channel estimate C(n) which was dominant prior
to fading will also dominate the channel estimate subsequent to
fading, i.e. when the signal strength F again increases. When
choosing a coefficient in the matrix Q it is assumed that the
mutual relationship between the energy transmitted along the
separate signal paths has not been changed to any appreciable
extent during fading. Subsequent to fading, when the signal
strength exceeds the threshold value FO, the coefficients in the
matrix Q are set to their original values, which may be q.sub.i =1
in all instances for example.
The threshold value FO can be determined in several different ways.
According to one simple alternative, FO may be constant. The
drawback with this alternative, however, is the difficulty
experienced in adapting to separate fading instances. In the case
of a pronounced single beam illustrated in FIG. 4, where the signal
Y reaches the area between the buildings 20 and 21 via practically
solely one propagation path, one of the coefficients in C(n), for
instance c.sub.l (n) will dominate. Thus, should the signal
strength F begin to fall it will be positively known that fading
has occurred and that the threshold value FO can be set to a
relatively high level. When the signal strength falls beneath FO,
q.sub.l is set to 1 and the remaining coefficients in the matrix Q
are set to 0. However, it may be so that the single path
propagation is less pronounced, so that the signal Y also reaches
the region of the mobile 22 via a reflected propagation path. The
reflected signal is relatively weak and the corresponding
coefficient in the channel estimate C(n), for instance c.sub.2 (n),
is much smaller than c.sub.l (n). If the threshold value FO is
constant and relatively high, fading will be indicated by the
mean-value-forming circuit 15, also in this propagation instance
when the signal strength F falls. Because the signal Y also reaches
the mobile 22 via a reflected propagation path, fading will not
occur, however, although the LMS-algorithm in the circuit 10 is
still controlled by the circuit 15 as though fading had actually
occurred. This impairs the transmission quality of the information
in the data sequence DO. In order to avoid this, the threshold
value FO can be determined in a more complicated method, in
accordance with the following. It is desirable to take into account
both the dominating coefficients in the channel estimate C(n),
according to the example c.sub.l (n) and c.sub.2 (n), which is the
next largest coefficient. In this case, the threshold value FO can
then be calculated in accordance with the relationship FO.sup.2
=H.u.sub.l (n)/u.sub.2 (n), where H is a constant. During the
period over which the signal strength falls beneath the threshold
value FO, q.sub.l is set to 1 and the remaining coefficients in the
matrix Q are set to 0, in accordance with the aforegoing.
According to one alternative, channel estimation can be controlled
with the aid of said LMS-algorithm in the following manner. The
matrix Q is held constant throughout the whole period, for instance
Q is the unit matrix. The coefficients in the matrix M are set to
the values which correspond to the coefficients in the
mean-value-forming channel estimate U(n), for instance .mu..sub.i
=R{u.sub.i (n).vertline.u.sub.h (n)}.sup.1/2 for i=1, ...., K,
where R is a constant and u.sub.h (n) is the largest of the energy
values u.sub.i (n). The coefficients .mu..sub.i will hereby become
weighting constants when controlling the LMS-algorithm. During a
fading sequence, the time interval TF, the coefficients in the
channel estimate C(n) will take very small values. Subsequent to
fading, the coefficients c.sub.i (n) will be adapted at a speed
proportional to the values .mu..sub.i in the matrix M. This means
that the filter coefficient, described with the numeral value
c.sub.h (n), which dominated the channel estimate C(n) over a
period prior to fading, will be the quickest to recover when fading
ceases. This alternative method of controlling the LMS-algorithm
also assumes that the mutual relationship between the energy
transmitted along the separate signal paths will not have changed
to any appreciable extent during the actual course of fading. No
indication that fading has occurred is required when controlling in
accordance with this second alternative. The coefficients in the
matrix Q may be constant over the whole period the coefficients
.mu..sub.i in the matrix M can be calculated constantly with a
starting point from the mean value of the impulse response energy
formed in accordance with the above.
According to the described examples, an LMS-algorithm is performed
in the circuit 10. The channel estimation can be effected, however,
with other types of algorithms. One example in this respect is the
RLS-algorithm, Recursive Least Squares, which is faster than the
LMS-algorithm, but much more complicated.
According to the above example, the equalizer 5 has only one
circuit, for calculating the estimated impulse response C(n), i.e.
the channel estimation filter CEST. However, it is possible, in
accordance with the invention, to use an equalizer which possesses
several circuits for estimating the channel impulse response, as
illustrated in FIG. 8. A viterbi equalizer 11 includes a viterbi
analyzer VIT1 with a number of states P=16, and a channel
estimating order which includes channel estimating circuits
CEST1....CEST16. A separate channel estimate C.sub.i (n) is formed
in these circuits for each state i of the viterbi algorithm. The
viterbi analyzer VIT1 receives the signal y(n) at the sampling time
point n and produces the estimated symbol S (n-L) after a delay
corresponding to L sampling intervals in the same manner as that
described for the viterbi analyzer VIT. All of the channel
estimating circuits are connected to the viterbi analyzer VIT1 and
an estimation of one of the part estimates C.sub.l (n) ....C.sub.16
(n) is effected in each of the channel estimating circuits. The
part estimate C.sub.j (n) for the new state j at the sampling time
point n is calculated iteratively from the part estimate C.sub.i
(n-1) for the old state i at a preceding sampling time point (n-1).
C.sub.i (n-1) is the channel estimate which belongs to the path
selected by VIT1 at the transition of state i to state j. C.sub.j
(n) is calculated with the aid of a transition error signal
e.sub.ij (n), which is calculated according to the relationship
e.sub.ij (n)=y(n)-C.sub.i.sup.T (n-1). S.sub.ij. In this case
S.sub.ij a transition vector with symbols for the old state i and
the new state j. The part estimate C.sub.j (n) for the new state j
at the sampling time point n is calculated in the j:th channel
estimating circuit according to the desired algorithm, for instance
the aforesaid LMS-algorithm, which gives
In FIG. 8 the channel estimation has been illustrated for the first
and the last state 1 and 16 respectively. At the sampling time
point n, the viterbi analyzer VIT1 selects a transition from the
state i to the state 1 and delivers the transition vector S.sub.il,
the transition error signal e.sub.il and the old part estimate
C.sub.i (n-1) to the channel estimating circuit CEST1. The new part
estimate C.sub.l (n) is calculated in the circuit CEST1 and is
delivered to VIT1 for use at the next time point (n+1) in the
viterbi detection, for continued iterative calculation of the part
estimates. In a similar manner, the viterbi analyzer VIT1 selects a
transition from the state p to the state 16 and delivers the
transition vector S.sub.p16, the transition error signal e.sub.p16
and the old part estimate C.sub.p (n-1) to CEST16. The new part
estimate C.sub.16 (n) is calculated and delivered to VIT1. Each of
the channel estimating circuits CEST1 up to and including CEST16 is
connected to a mean value forming circuit 17. This circuit controls
the adaptation algorithm through the signal U(n) in a manner
corresponding to that described with reference to the mean value
forming circuit 15 of the channel estimating filter CEST according
to FIG. 7. The circuit 17 utilizes, for instance, the channel
estimate obtained with the aid of solely the synchronizing
sequences SO for its mean-value-forming function.
In the aforegoing the invention has been described with reference
to the equalizers 5 and 11, both of which have viterbi analyzers
VIT and VIT1 respectively. The invention permits, however, the use
of other types of equalizer, which are connected to channel
estimating circuits whose adaptation algorithm is controlled with
the aid of the mean energy value U(n) for the channel estimate
C(n).
* * * * *